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An SVM-like approach for expectile regression

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  • Farooq, Muhammad
  • Steinwart, Ingo

Abstract

Expectile regression is an interesting tool for investigating conditional distributions beyond the conditional mean. It is well-known that expectiles can be described with the help of the asymmetric least square loss function, and this link makes it possible to estimate expectiles in a non-parametric framework with a support vector machine like approach. For the underlying optimization problem, an efficient sequential-minimal-optimization-based solver is developed and its convergence derived. The behavior of the solver is investigated by conducting various experiments, and the results are compared with the solver for quantile regression and the recent R-package ER-Boost.

Suggested Citation

  • Farooq, Muhammad & Steinwart, Ingo, 2017. "An SVM-like approach for expectile regression," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 159-181.
    • Handle: RePEc:eee:csdana:v:109:y:2017:i:c:p:159-181
    DOI: 10.1016/j.csda.2016.11.010
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    References listed on IDEAS

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    1. Huang, Xiaolin & Shi, Lei & Suykens, Johan A.K., 2014. "Asymmetric least squares support vector machine classifiers," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 395-405.
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    Cited by:

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    2. Songfeng Zheng, 2021. "KLERC: kernel Lagrangian expectile regression calculator," Computational Statistics, Springer, vol. 36(1), pages 283-311, March.

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